
"""simple docstring"""



import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch
 from torch import nn

 from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
 from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import DPTImageProcessor




class 		a_    :







    def __init__(      self      :							List[str]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Union[str, Any]=2  ,       __UpperCamelCase      :							str=32  ,       __UpperCamelCase      :							Optional[Any]=16  ,       __UpperCamelCase      :							List[Any]=3  ,       __UpperCamelCase      :							Union[str, Any]=True  ,       __UpperCamelCase      :							Any=True  ,       __UpperCamelCase      :							Dict=32  ,       __UpperCamelCase      :							Dict=4  ,       __UpperCamelCase      :							Optional[Any]=[0, 1, 2, 3]  ,       __UpperCamelCase      :							List[str]=4  ,       __UpperCamelCase      :							Any=37  ,       __UpperCamelCase      :							str="gelu"  ,       __UpperCamelCase      :							Tuple=0.1  ,       __UpperCamelCase      :							List[Any]=0.1  ,       __UpperCamelCase      :							List[str]=0.0_2  ,       __UpperCamelCase      :							Optional[Any]=3  ,       __UpperCamelCase      :							str=[1, 3_84, 24, 24]  ,       __UpperCamelCase      :							Optional[int]=True  ,       __UpperCamelCase      :							int=None  ,       )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = parent
        _UpperCAmelCase        = batch_size
        _UpperCAmelCase        = image_size
        _UpperCAmelCase        = patch_size
        _UpperCAmelCase        = num_channels
        _UpperCAmelCase        = is_training
        _UpperCAmelCase        = use_labels
        _UpperCAmelCase        = hidden_size
        _UpperCAmelCase        = num_hidden_layers
        _UpperCAmelCase        = backbone_out_indices
        _UpperCAmelCase        = num_attention_heads
        _UpperCAmelCase        = intermediate_size
        _UpperCAmelCase        = hidden_act
        _UpperCAmelCase        = hidden_dropout_prob
        _UpperCAmelCase        = attention_probs_dropout_prob
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = num_labels
        _UpperCAmelCase        = backbone_featmap_shape
        _UpperCAmelCase        = scope
        _UpperCAmelCase        = is_hybrid
        # sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
        _UpperCAmelCase        = (image_size // patch_size) ** 2
        _UpperCAmelCase        = num_patches + 1







    def   _snake_case       (      self      :							int  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

        _UpperCAmelCase        = None
        if self.use_labels:
            _UpperCAmelCase        = ids_tensor([self.batch_size, self.image_size, self.image_size]  ,       self.num_labels  )

        _UpperCAmelCase        = self.get_config()

        return config, pixel_values, labels







    def   _snake_case       (      self      :							Any  )		->Optional[Any]:






        '''simple docstring'''






        _UpperCAmelCase        = {
            """global_padding""": """same""",
            """layer_type""": """bottleneck""",
            """depths""": [3, 4, 9],
            """out_features""": ["""stage1""", """stage2""", """stage3"""],
            """embedding_dynamic_padding""": True,
            """hidden_sizes""": [96, 1_92, 3_84, 7_68],
            """num_groups""": 2,
        }

        return DPTConfig(
            image_size=self.image_size  ,       patch_size=self.patch_size  ,       num_channels=self.num_channels  ,       hidden_size=self.hidden_size  ,       num_hidden_layers=self.num_hidden_layers  ,       backbone_out_indices=self.backbone_out_indices  ,       num_attention_heads=self.num_attention_heads  ,       intermediate_size=self.intermediate_size  ,       hidden_act=self.hidden_act  ,       hidden_dropout_prob=self.hidden_dropout_prob  ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob  ,       is_decoder=__UpperCamelCase  ,       initializer_range=self.initializer_range  ,       is_hybrid=self.is_hybrid  ,       backbone_config=__UpperCamelCase  ,       backbone_featmap_shape=self.backbone_featmap_shape  ,       )







    def   _snake_case       (      self      :							List[str]  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							Dict  ,       __UpperCamelCase      :							int  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = DPTModel(config=__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  )
        self.parent.assertEqual(result.last_hidden_state.shape  ,       (self.batch_size, self.seq_length, self.hidden_size)  )







    def   _snake_case       (      self      :							str  ,       __UpperCamelCase      :							str  ,       __UpperCamelCase      :							int  ,       __UpperCamelCase      :							int  )		->Optional[int]:






        '''simple docstring'''






        _UpperCAmelCase        = self.num_labels
        _UpperCAmelCase        = DPTForDepthEstimation(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  )
        self.parent.assertEqual(result.predicted_depth.shape  ,       (self.batch_size, self.image_size, self.image_size)  )







    def   _snake_case       (      self      :							Dict  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Tuple  ,       __UpperCamelCase      :							Optional[int]  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = self.num_labels
        _UpperCAmelCase        = DPTForSemanticSegmentation(__UpperCamelCase  )
        model.to(__UpperCamelCase  )
        model.eval()
        _UpperCAmelCase        = model(__UpperCamelCase  ,       labels=__UpperCamelCase  )
        self.parent.assertEqual(
            result.logits.shape  ,       (self.batch_size, self.num_labels, self.image_size, self.image_size)  )







    def   _snake_case       (      self      :							List[str]  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = self.prepare_config_and_inputs()
        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = config_and_inputs
        _UpperCAmelCase        = {"""pixel_values""": pixel_values}
        return config, inputs_dict







@require_torch
class 		a_    (			_UpperCAmelCase  ,    _UpperCAmelCase  ,    unittest.TestCase						):
    a   :					Optional[Any]			     =					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
    a   :					Union[str, Any]			     =					(
        {
            'depth-estimation': DPTForDepthEstimation,
            'feature-extraction': DPTModel,
            'image-segmentation': DPTForSemanticSegmentation,
        }
        if is_torch_available()
        else {}
    )

    a   :					Optional[Any]			     =					False
    a   :					Tuple			     =					False
    a   :					Optional[Any]			     =					False







    def   _snake_case       (      self      :							str  )		->str:






        '''simple docstring'''






        _UpperCAmelCase        = DPTModelTester(self  )
        _UpperCAmelCase        = ConfigTester(self  ,       config_class=__UpperCamelCase  ,       has_text_modality=__UpperCamelCase  ,       hidden_size=37  )







    def   _snake_case       (      self      :							Union[str, Any]  )		->Union[str, Any]:






        '''simple docstring'''






        self.config_tester.run_common_tests()







    @unittest.skip(reason="""DPT does not use inputs_embeds"""  )
    def   _snake_case       (      self      :							str  )		->int:






        '''simple docstring'''






        pass







    def   _snake_case       (      self      :							List[Any]  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            _UpperCAmelCase        = model_class(__UpperCamelCase  )
            self.assertIsInstance(model.get_input_embeddings()  ,       (nn.Module)  )
            _UpperCAmelCase        = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(__UpperCamelCase  ,       nn.Linear  )  )







    def   _snake_case       (      self      :							List[str]  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            _UpperCAmelCase        = model_class(__UpperCamelCase  )
            _UpperCAmelCase        = inspect.signature(model.forward  )
            # signature.parameters is an OrderedDict => so arg_names order is deterministic
            _UpperCAmelCase        = [*signature.parameters.keys()]

            _UpperCAmelCase        = ["""pixel_values"""]
            self.assertListEqual(arg_names[:1]  ,       __UpperCamelCase  )







    def   _snake_case       (      self      :							List[str]  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*__UpperCamelCase  )







    def   _snake_case       (      self      :							Tuple  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_depth_estimation(*__UpperCamelCase  )







    def   _snake_case       (      self      :							str  )		->List[str]:






        '''simple docstring'''






        _UpperCAmelCase        = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_semantic_segmentation(*__UpperCamelCase  )







    def   _snake_case       (      self      :							List[Any]  )		->Dict:






        '''simple docstring'''






        for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                continue

            _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
            _UpperCAmelCase        = True

            if model_class in get_values(__UpperCamelCase  ):
                continue

            _UpperCAmelCase        = model_class(__UpperCamelCase  )
            model.to(__UpperCamelCase  )
            model.train()
            _UpperCAmelCase        = self._prepare_for_class(__UpperCamelCase  ,       __UpperCamelCase  ,       return_labels=__UpperCamelCase  )
            _UpperCAmelCase        = model(**__UpperCamelCase  ).loss
            loss.backward()







    def   _snake_case       (      self      :							Dict  )		->Union[str, Any]:






        '''simple docstring'''






        for model_class in self.all_model_classes:
            if model_class.__name__ == "DPTForDepthEstimation":
                continue

            _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
            _UpperCAmelCase        = False
            _UpperCAmelCase        = True

            if model_class in get_values(__UpperCamelCase  ) or not model_class.supports_gradient_checkpointing:
                continue
            _UpperCAmelCase        = model_class(__UpperCamelCase  )
            model.to(__UpperCamelCase  )
            model.gradient_checkpointing_enable()
            model.train()
            _UpperCAmelCase        = self._prepare_for_class(__UpperCamelCase  ,       __UpperCamelCase  ,       return_labels=__UpperCamelCase  )
            _UpperCAmelCase        = model(**__UpperCamelCase  ).loss
            loss.backward()







    def   _snake_case       (      self      :							List[str]  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()

        _UpperCAmelCase        = _config_zero_init(__UpperCamelCase  )
        for model_class in self.all_model_classes:
            _UpperCAmelCase        = model_class(config=__UpperCamelCase  )
            # Skip the check for the backbone
            _UpperCAmelCase        = []
            for name, module in model.named_modules():
                if module.__class__.__name__ == "DPTViTHybridEmbeddings":
                    _UpperCAmelCase        = [f"""{name}.{key}""" for key in module.state_dict().keys()]
                    break

            for name, param in model.named_parameters():
                if param.requires_grad:
                    if name in backbone_params:
                        continue
                    self.assertIn(
                        ((param.data.mean() * 1e9).round() / 1e9).item()  ,       [0.0, 1.0]  ,       msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""  ,       )







    @unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests."""  )
    def   _snake_case       (      self      :							Union[str, Any]  )		->List[Any]:






        '''simple docstring'''






        pass







    @slow
    def   _snake_case       (      self      :							Tuple  )		->Dict:






        '''simple docstring'''






        for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
            _UpperCAmelCase        = DPTModel.from_pretrained(__UpperCamelCase  )
            self.assertIsNotNone(__UpperCamelCase  )







    def   _snake_case       (      self      :							List[Any]  )		->Union[str, Any]:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase        = self.model_tester.prepare_config_and_inputs_for_common()
        _UpperCAmelCase        = """add"""
        with self.assertRaises(__UpperCamelCase  ):
            _UpperCAmelCase        = DPTForDepthEstimation(__UpperCamelCase  )







def 			_UpperCamelCase						(    )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
    return image







@require_torch
@require_vision
@slow
class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							str  )		->Tuple:






        '''simple docstring'''






        _UpperCAmelCase        = DPTImageProcessor.from_pretrained("""Intel/dpt-hybrid-midas"""  )
        _UpperCAmelCase        = DPTForDepthEstimation.from_pretrained("""Intel/dpt-hybrid-midas"""  ).to(__UpperCamelCase  )

        _UpperCAmelCase        = prepare_img()
        _UpperCAmelCase        = image_processor(images=__UpperCamelCase  ,       return_tensors="""pt"""  ).to(__UpperCamelCase  )

        # forward pass
        with torch.no_grad():
            _UpperCAmelCase        = model(**__UpperCamelCase  )
            _UpperCAmelCase        = outputs.predicted_depth

        # verify the predicted depth
        _UpperCAmelCase        = torch.Size((1, 3_84, 3_84)  )
        self.assertEqual(predicted_depth.shape  ,       __UpperCamelCase  )

        _UpperCAmelCase        = torch.tensor(
            [[[5.6_4_3_7, 5.6_1_4_6, 5.6_5_1_1], [5.4_3_7_1, 5.5_6_4_9, 5.5_9_5_8], [5.5_2_1_5, 5.5_1_8_4, 5.5_2_9_3]]]  ).to(__UpperCamelCase  )

        self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00  ,       __UpperCamelCase  ,       atol=1e-4  )  )

"""simple docstring"""



from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


a       :						List[Any]       		=		logging.get_logger(__name__)

a       :						str       		=		{
    '''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
    '''umberto-commoncrawl-cased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
    ),
    '''umberto-wikipedia-uncased-v1''': (
        '''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
    ),
}




class 		a_    (			_UpperCAmelCase						):
    a   :					Any			     =					'camembert'







    def __init__(      self      :							Dict  ,       __UpperCamelCase      :							Union[str, Any]=3_05_22  ,       __UpperCamelCase      :							Tuple=7_68  ,       __UpperCamelCase      :							Optional[Any]=12  ,       __UpperCamelCase      :							int=12  ,       __UpperCamelCase      :							int=30_72  ,       __UpperCamelCase      :							int="gelu"  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							Dict=0.1  ,       __UpperCamelCase      :							List[Any]=5_12  ,       __UpperCamelCase      :							Optional[int]=2  ,       __UpperCamelCase      :							Dict=0.0_2  ,       __UpperCamelCase      :							Optional[int]=1e-12  ,       __UpperCamelCase      :							Optional[Any]=1  ,       __UpperCamelCase      :							int=0  ,       __UpperCamelCase      :							Optional[int]=2  ,       __UpperCamelCase      :							Optional[int]="absolute"  ,       __UpperCamelCase      :							int=True  ,       __UpperCamelCase      :							Dict=None  ,       **__UpperCamelCase      :							List[str]  ,       )		->Union[str, Any]:






        '''simple docstring'''






        super().__init__(pad_token_id=__UpperCamelCase  ,       bos_token_id=__UpperCamelCase  ,       eos_token_id=__UpperCamelCase  ,       **__UpperCamelCase  )

        _UpperCAmelCase        = vocab_size
        _UpperCAmelCase        = hidden_size
        _UpperCAmelCase        = num_hidden_layers
        _UpperCAmelCase        = num_attention_heads
        _UpperCAmelCase        = hidden_act
        _UpperCAmelCase        = intermediate_size
        _UpperCAmelCase        = hidden_dropout_prob
        _UpperCAmelCase        = attention_probs_dropout_prob
        _UpperCAmelCase        = max_position_embeddings
        _UpperCAmelCase        = type_vocab_size
        _UpperCAmelCase        = initializer_range
        _UpperCAmelCase        = layer_norm_eps
        _UpperCAmelCase        = position_embedding_type
        _UpperCAmelCase        = use_cache
        _UpperCAmelCase        = classifier_dropout







class 		a_    (			_UpperCAmelCase						):







    @property
    def   _snake_case       (      self      :							Optional[int]  )		->Mapping[str, Mapping[int, str]]:






        '''simple docstring'''






        if self.task == "multiple-choice":
            _UpperCAmelCase        = {0: """batch""", 1: """choice""", 2: """sequence"""}
        else:
            _UpperCAmelCase        = {0: """batch""", 1: """sequence"""}
        return OrderedDict(
            [
                ("""input_ids""", dynamic_axis),
                ("""attention_mask""", dynamic_axis),
            ]  )